





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "swinv2"

							lowerCamelCase_							     =       {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(				self     :int				,					__A     :Tuple=224				,					__A     :Any=4				,					__A     :int=3				,					__A     :Optional[int]=96				,					__A     :Tuple=[2, 2, 6, 2]				,					__A     :Dict=[3, 6, 12, 24]				,					__A     :Dict=7				,					__A     :List[Any]=4.0				,					__A     :Tuple=True				,					__A     :Dict=0.0				,					__A     :Optional[Any]=0.0				,					__A     :List[Any]=0.1				,					__A     :int="gelu"				,					__A     :List[str]=False				,					__A     :Optional[int]=0.0_2				,					__A     :Optional[Any]=1E-5				,					__A     :str=32				,					**__A     :Union[str, Any]				,					) -> Dict:



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__								=   image_size
									SCREAMING_SNAKE_CASE__								=   patch_size
									SCREAMING_SNAKE_CASE__								=   num_channels
									SCREAMING_SNAKE_CASE__								=   embed_dim
									SCREAMING_SNAKE_CASE__								=   depths
									SCREAMING_SNAKE_CASE__								=   len(__A		)
									SCREAMING_SNAKE_CASE__								=   num_heads
									SCREAMING_SNAKE_CASE__								=   window_size
									SCREAMING_SNAKE_CASE__								=   mlp_ratio
									SCREAMING_SNAKE_CASE__								=   qkv_bias
									SCREAMING_SNAKE_CASE__								=   hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=   attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=   drop_path_rate
									SCREAMING_SNAKE_CASE__								=   hidden_act
									SCREAMING_SNAKE_CASE__								=   use_absolute_embeddings
									SCREAMING_SNAKE_CASE__								=   layer_norm_eps
									SCREAMING_SNAKE_CASE__								=   initializer_range
									SCREAMING_SNAKE_CASE__								=   encoder_stride
									# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									SCREAMING_SNAKE_CASE__								=   int(embed_dim * 2 ** (len(__A		) - 1)		)
									SCREAMING_SNAKE_CASE__								=   (0, 0, 0, 0)
_SCREAMING_SNAKE_CASE 		=      9.8_06_65





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_ = g       )		->	float:







 '''simple docstring'''

 if fluid_density <= 0:
  raise ValueError("""Impossible fluid density"""       )
 if volume < 0:
  raise ValueError("""Impossible Object volume"""       )
 if gravity <= 0:
  raise ValueError("""Impossible Gravity"""       )

 return fluid_density * gravity * volume


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()
